| Book Name: | Algorithms for Decision Making |
| Category: | Algorithms |
| Free Download: | Available |
Algorithms for Decision Making
Book Description
This book provides a general introduction to algorithms for decision making under conditions of uncertainty. It covers a wide range of topics related to decision making, introducing the formulas of basic mathematical problems and the algorithms to solve them. Figures, examples, and exercises are provided to convey the intuition behind the different approaches.
It requires a certain level of mathematics and assumes prior exposure to multivariable calculus, linear algebra, and concepts of probability. Some review materials are provided in the appendix.
Subjects where the book will be particularly useful include mathematics, statistics, computer science, aerospace, electrical engineering, and operations studies.
The basis of this manual are algorithms, all implemented in the Julia programming language.
Algorithms for Decision Making PDF
A general introduction to algorithms for decision making under uncertain conditions, and an introduction to the formulations of fundamental mathematical problems and the algorithms for solving them.
Automated decision-making or decision support systems – used in applications ranging from aircraft collision avoidance to breast cancer screening – must be designed to account for various sources of uncertainty while carefully balancing multiple goals. This textbook provides a general introduction to algorithms for decision making under conditions of uncertainty, including formulas for basic mathematical problems and algorithms for solving them.
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The book first deals with the reasoning problem of uncertainty and the objective in simple decisions at a given time, then moves on to sequential decision problems in a random environment that The outcome of our actions is uncertain. He then discusses model uncertainty, when we don’t start from a known model and must learn how acting interacts with the environment; uncertain state, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple actors. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised and optimized learning. The algorithms are implemented in the Julia programming language. The figures, examples, and exercises reflect the intuition behind the different approaches presented.
Algorithms for Decision Making
Author(s): Mykel J. Kochenderfer, Tim A. Wheeler, Kyle H. Wray
Publisher: The MIT Press, Year: 2022
ISBN: 0262047012,9780262047012
